A new alternating direction trust region method based on conic model for solving unconstrained optimization. (3rd July 2021)

• Record Type: Journal Article
• Authors: Zhu, Honglan; Ni, Qin; Jiang, Jianlin; Dang, Chuangyin
• Is Part Of: Optimization. Volume 70:Number 7(2021)
• Page Start: 1555
• Page End: 1579
• Publication Date: 2021-07-03
• Subjects: Unconstrained optimization -- conic model -- trust region method -- alternating direction search method -- global convergence; 65k -- 90c; Mathematical optimization -- Periodicals; 519.7
• DOI: 10.1080/02331934.2020.1745793
• Languages: English
• ISSNs: 0233-1934

Abstract

ABSTRACT: In this paper, a new alternating direction trust region method based on conic model is used to solve unconstrained optimization problems. By use of the alternating direction search method, the new conic model trust region subproblem is solved by two steps in two orthogonal directions. This new idea overcomes the shortcomings of conic model subproblem which is difficult to solve. Then the global convergence of the method under some reasonable conditions is established. Numerical experiment shows that this method may be better than the dogleg method to solve the subproblem, especially for large-scale problems.